Human emotion can be expressed across two channels which is via verbal and non-verbal. Verbal way of expression include through speech, sound and text whereas Non-Verbal way of expression include facial expressions and gestures. In today’s technological world, a majority of population across the world prefer text as a common channel for sharing their opinions or emotions through social media as well as product reviews in e-commerce platforms. Emotion recognition is an affective computing development system utilized in the process of detecting, predicting human emotional state such as anger, sad, happiness etc. The main aim of this study is to develop an emotion recognition system for text-based content. All models were tested using criteria from the ISEAR (International Survey of Antecedents and Affective Reactions) dataset. In this study supervised machine learning algorithms were used to develop a model based on six basic emotions which are sadness, anger, love, fear, joy, surprise. Emotion prediction system for text-based content was successfully developed.